Overview

Dataset statistics

Number of variables13
Number of observations202
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory104.6 B

Variable types

Categorical2
Numeric11

Alerts

PlayerName has a high cardinality: 198 distinct values High cardinality
DistanceCovered(InKms) is highly correlated with MinutestoGoalRatio and 2 other fieldsHigh correlation
MinutestoGoalRatio is highly correlated with DistanceCovered(InKms) and 3 other fieldsHigh correlation
ShotsPerGame is highly correlated with DistanceCovered(InKms) and 3 other fieldsHigh correlation
BMI is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Cost is highly correlated with ScoreHigh correlation
PreviousClubCost is highly correlated with DistanceCovered(InKms) and 5 other fieldsHigh correlation
Height is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Weight is highly correlated with BMI and 2 other fieldsHigh correlation
Score is highly correlated with MinutestoGoalRatio and 2 other fieldsHigh correlation
DistanceCovered(InKms) is highly correlated with MinutestoGoalRatio and 2 other fieldsHigh correlation
MinutestoGoalRatio is highly correlated with DistanceCovered(InKms) and 3 other fieldsHigh correlation
ShotsPerGame is highly correlated with DistanceCovered(InKms) and 3 other fieldsHigh correlation
BMI is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Cost is highly correlated with ScoreHigh correlation
PreviousClubCost is highly correlated with DistanceCovered(InKms) and 5 other fieldsHigh correlation
Height is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Weight is highly correlated with BMI and 2 other fieldsHigh correlation
Score is highly correlated with MinutestoGoalRatio and 2 other fieldsHigh correlation
DistanceCovered(InKms) is highly correlated with MinutestoGoalRatio and 1 other fieldsHigh correlation
MinutestoGoalRatio is highly correlated with DistanceCovered(InKms) and 1 other fieldsHigh correlation
ShotsPerGame is highly correlated with DistanceCovered(InKms) and 1 other fieldsHigh correlation
BMI is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Cost is highly correlated with ScoreHigh correlation
PreviousClubCost is highly correlated with BMI and 2 other fieldsHigh correlation
Height is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Weight is highly correlated with BMI and 2 other fieldsHigh correlation
Score is highly correlated with CostHigh correlation
Club is highly correlated with DistanceCovered(InKms) and 5 other fieldsHigh correlation
DistanceCovered(InKms) is highly correlated with Club and 2 other fieldsHigh correlation
MinutestoGoalRatio is highly correlated with Club and 3 other fieldsHigh correlation
ShotsPerGame is highly correlated with Club and 6 other fieldsHigh correlation
BMI is highly correlated with ShotsPerGame and 2 other fieldsHigh correlation
Cost is highly correlated with Club and 1 other fieldsHigh correlation
PreviousClubCost is highly correlated with Club and 5 other fieldsHigh correlation
Height is highly correlated with PreviousClubCost and 1 other fieldsHigh correlation
Weight is highly correlated with ShotsPerGame and 3 other fieldsHigh correlation
Score is highly correlated with Club and 2 other fieldsHigh correlation
PlayerName is uniformly distributed Uniform

Reproduction

Analysis started2022-12-10 05:04:54.052869
Analysis finished2022-12-10 05:05:10.587219
Duration16.53 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

PlayerName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct198
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Rice, Master. Eugene
 
2
Rice, Master. Eric
 
2
Rogers, Mr. William John
 
2
Rice, Master. Arthur
 
2
Nasser, Mr. Nicholas
 
1
Other values (193)
193 

Length

Max length47
Median length33
Mean length23.79207921
Min length13

Characters and Unicode

Total characters4806
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)96.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowAllen, Mr. William Henry
3rd rowMoran, Mr. James
4th rowMcCarthy, Mr. Timothy J
5th rowPalsson, Master. Gosta Leonard

Common Values

ValueCountFrequency (%)
Rice, Master. Eugene2
 
1.0%
Rice, Master. Eric2
 
1.0%
Rogers, Mr. William John2
 
1.0%
Rice, Master. Arthur2
 
1.0%
Nasser, Mr. Nicholas1
 
0.5%
Pernot, Mr. Rene1
 
0.5%
Nicholls, Mr. Joseph Charles1
 
0.5%
Andrew, Mr. Edgardo Samuel1
 
0.5%
Pekoniemi, Mr. Edvard1
 
0.5%
Petroff, Mr. Pastcho ("Pentcho")1
 
0.5%
Other values (188)188
93.1%

Length

2022-12-10T10:35:10.679087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr176
 
23.2%
master22
 
2.9%
william20
 
2.6%
john13
 
1.7%
henry12
 
1.6%
charles10
 
1.3%
richard8
 
1.1%
james7
 
0.9%
rice7
 
0.9%
george7
 
0.9%
Other values (383)476
62.8%

Most occurring characters

ValueCountFrequency (%)
556
 
11.6%
r459
 
9.6%
e354
 
7.4%
a315
 
6.6%
n252
 
5.2%
M230
 
4.8%
i215
 
4.5%
o215
 
4.5%
l203
 
4.2%
,202
 
4.2%
Other values (47)1805
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3062
63.7%
Uppercase Letter757
 
15.8%
Space Separator556
 
11.6%
Other Punctuation420
 
8.7%
Close Punctuation5
 
0.1%
Open Punctuation5
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r459
15.0%
e354
11.6%
a315
10.3%
n252
8.2%
i215
 
7.0%
o215
 
7.0%
l203
 
6.6%
s192
 
6.3%
t140
 
4.6%
h111
 
3.6%
Other values (16)606
19.8%
Uppercase Letter
ValueCountFrequency (%)
M230
30.4%
R46
 
6.1%
A45
 
5.9%
J45
 
5.9%
H43
 
5.7%
C42
 
5.5%
S41
 
5.4%
W34
 
4.5%
E33
 
4.4%
F27
 
3.6%
Other values (14)171
22.6%
Other Punctuation
ValueCountFrequency (%)
,202
48.1%
.202
48.1%
"16
 
3.8%
Space Separator
ValueCountFrequency (%)
556
100.0%
Close Punctuation
ValueCountFrequency (%)
)5
100.0%
Open Punctuation
ValueCountFrequency (%)
(5
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3819
79.5%
Common987
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r459
 
12.0%
e354
 
9.3%
a315
 
8.2%
n252
 
6.6%
M230
 
6.0%
i215
 
5.6%
o215
 
5.6%
l203
 
5.3%
s192
 
5.0%
t140
 
3.7%
Other values (40)1244
32.6%
Common
ValueCountFrequency (%)
556
56.3%
,202
 
20.5%
.202
 
20.5%
"16
 
1.6%
)5
 
0.5%
(5
 
0.5%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
556
 
11.6%
r459
 
9.6%
e354
 
7.4%
a315
 
6.6%
n252
 
5.2%
M230
 
4.8%
i215
 
4.5%
o215
 
4.5%
l203
 
4.2%
,202
 
4.2%
Other values (47)1805
37.6%

Club
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
CHE
78 
LIV
65 
MUN
59 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters606
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMUN
2nd rowMUN
3rd rowMUN
4th rowMUN
5th rowMUN

Common Values

ValueCountFrequency (%)
CHE78
38.6%
LIV65
32.2%
MUN59
29.2%

Length

2022-12-10T10:35:10.782267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-10T10:35:10.878466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
che78
38.6%
liv65
32.2%
mun59
29.2%

Most occurring characters

ValueCountFrequency (%)
C78
12.9%
H78
12.9%
E78
12.9%
L65
10.7%
I65
10.7%
V65
10.7%
M59
9.7%
U59
9.7%
N59
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter606
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C78
12.9%
H78
12.9%
E78
12.9%
L65
10.7%
I65
10.7%
V65
10.7%
M59
9.7%
U59
9.7%
N59
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin606
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C78
12.9%
H78
12.9%
E78
12.9%
L65
10.7%
I65
10.7%
V65
10.7%
M59
9.7%
U59
9.7%
N59
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C78
12.9%
H78
12.9%
E78
12.9%
L65
10.7%
I65
10.7%
V65
10.7%
M59
9.7%
U59
9.7%
N59
9.7%

DistanceCovered(InKms)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct114
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.718613861
Minimum3.8
Maximum6.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:10.978760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.0315
Q14.3725
median4.755
Q35.03
95-th percentile5.378
Maximum6.72
Range2.92
Interquartile range (IQR)0.6575

Descriptive statistics

Standard deviation0.4579763615
Coefficient of variation (CV)0.09705739332
Kurtosis0.7099545836
Mean4.718613861
Median Absolute Deviation (MAD)0.345
Skewness0.4191233292
Sum953.16
Variance0.2097423477
MonotonicityNot monotonic
2022-12-10T10:35:11.102757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.876
 
3.0%
4.465
 
2.5%
5.115
 
2.5%
4.714
 
2.0%
5.134
 
2.0%
4.834
 
2.0%
5.164
 
2.0%
4.514
 
2.0%
5.344
 
2.0%
5.034
 
2.0%
Other values (104)158
78.2%
ValueCountFrequency (%)
3.81
0.5%
3.92
1.0%
3.911
0.5%
3.952
1.0%
3.962
1.0%
41
0.5%
4.021
0.5%
4.031
0.5%
4.061
0.5%
4.071
0.5%
ValueCountFrequency (%)
6.721
0.5%
5.931
0.5%
5.691
0.5%
5.661
0.5%
5.591
0.5%
5.51
0.5%
5.491
0.5%
5.482
1.0%
5.41
0.5%
5.381
0.5%

Goals
Real number (ℝ≥0)

Distinct71
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.108663366
Minimum3.3
Maximum14.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:11.217897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile4.5
Q15.9
median6.85
Q38.275
95-th percentile10.095
Maximum14.3
Range11
Interquartile range (IQR)2.375

Descriptive statistics

Standard deviation1.800549031
Coefficient of variation (CV)0.2532893932
Kurtosis1.514969007
Mean7.108663366
Median Absolute Deviation (MAD)1.05
Skewness0.8413109131
Sum1435.95
Variance3.241976811
MonotonicityNot monotonic
2022-12-10T10:35:11.335872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.411
 
5.4%
6.69
 
4.5%
5.88
 
4.0%
7.58
 
4.0%
7.67
 
3.5%
8.96
 
3.0%
7.36
 
3.0%
66
 
3.0%
8.36
 
3.0%
6.15
 
2.5%
Other values (61)130
64.4%
ValueCountFrequency (%)
3.31
0.5%
3.91
0.5%
42
1.0%
4.11
0.5%
4.21
0.5%
4.32
1.0%
4.42
1.0%
4.52
1.0%
4.62
1.0%
4.71
0.5%
ValueCountFrequency (%)
14.31
0.5%
13.31
0.5%
12.91
0.5%
12.71
0.5%
10.91
0.5%
10.81
0.5%
10.71
0.5%
10.61
0.5%
10.21
0.5%
10.12
1.0%

MinutestoGoalRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct105
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09158416
Minimum35.9
Maximum59.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:11.455374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35.9
5-th percentile37.4
Q140.6
median43.5
Q345.575
95-th percentile48.2
Maximum59.7
Range23.8
Interquartile range (IQR)4.975

Descriptive statistics

Standard deviation3.662989354
Coefficient of variation (CV)0.08500475037
Kurtosis0.9439912887
Mean43.09158416
Median Absolute Deviation (MAD)2.5
Skewness0.2772877614
Sum8704.5
Variance13.41749101
MonotonicityNot monotonic
2022-12-10T10:35:11.576649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.35
 
2.5%
43.85
 
2.5%
41.45
 
2.5%
37.74
 
2.0%
41.14
 
2.0%
44.94
 
2.0%
44.84
 
2.0%
46.34
 
2.0%
39.73
 
1.5%
45.53
 
1.5%
Other values (95)161
79.7%
ValueCountFrequency (%)
35.91
0.5%
361
0.5%
36.31
0.5%
36.41
0.5%
36.52
1.0%
36.62
1.0%
36.91
0.5%
37.31
0.5%
37.42
1.0%
37.52
1.0%
ValueCountFrequency (%)
59.71
0.5%
50.51
0.5%
50.21
0.5%
49.81
0.5%
49.71
0.5%
49.51
0.5%
49.41
0.5%
49.11
0.5%
48.61
0.5%
48.31
0.5%

ShotsPerGame
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.56633663
Minimum11.6
Maximum19.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:11.695570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile12.405
Q113.5
median14.7
Q315.575
95-th percentile16.5
Maximum19.2
Range7.6
Interquartile range (IQR)2.075

Descriptive statistics

Standard deviation1.362451495
Coefficient of variation (CV)0.0935342584
Kurtosis0.03856895279
Mean14.56633663
Median Absolute Deviation (MAD)1
Skewness0.1772541073
Sum2942.4
Variance1.856274075
MonotonicityNot monotonic
2022-12-10T10:35:11.818996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.910
 
5.0%
159
 
4.5%
14.79
 
4.5%
14.88
 
4.0%
148
 
4.0%
15.27
 
3.5%
15.87
 
3.5%
14.47
 
3.5%
14.56
 
3.0%
14.96
 
3.0%
Other values (45)125
61.9%
ValueCountFrequency (%)
11.61
 
0.5%
11.81
 
0.5%
121
 
0.5%
12.12
 
1.0%
12.33
1.5%
12.43
1.5%
12.56
3.0%
12.63
1.5%
12.76
3.0%
12.83
1.5%
ValueCountFrequency (%)
19.21
 
0.5%
18.51
 
0.5%
181
 
0.5%
17.71
 
0.5%
17.31
 
0.5%
17.22
1.0%
16.81
 
0.5%
16.71
 
0.5%
16.53
1.5%
16.41
 
0.5%

AgentCharges
Real number (ℝ≥0)

Distinct111
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.87623762
Minimum8
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:11.943189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile22
Q141.25
median65.5
Q397
95-th percentile182.95
Maximum234
Range226
Interquartile range (IQR)55.75

Descriptive statistics

Standard deviation47.50123884
Coefficient of variation (CV)0.6178923463
Kurtosis1.486264774
Mean76.87623762
Median Absolute Deviation (MAD)25.5
Skewness1.290184321
Sum15529
Variance2256.367691
MonotonicityNot monotonic
2022-12-10T10:35:12.059101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416
 
3.0%
585
 
2.5%
445
 
2.5%
435
 
2.5%
364
 
2.0%
534
 
2.0%
504
 
2.0%
974
 
2.0%
734
 
2.0%
304
 
2.0%
Other values (101)157
77.7%
ValueCountFrequency (%)
81
 
0.5%
121
 
0.5%
131
 
0.5%
161
 
0.5%
191
 
0.5%
202
1.0%
212
1.0%
223
1.5%
251
 
0.5%
262
1.0%
ValueCountFrequency (%)
2341
0.5%
2331
0.5%
2201
0.5%
2141
0.5%
2131
0.5%
2122
1.0%
1911
0.5%
1891
0.5%
1841
0.5%
1831
0.5%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct180
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.95589109
Minimum16.75
Maximum34.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:12.180712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16.75
5-th percentile19.003
Q121.0825
median22.72
Q324.465
95-th percentile27.5515
Maximum34.42
Range17.67
Interquartile range (IQR)3.3825

Descriptive statistics

Standard deviation2.863932767
Coefficient of variation (CV)0.1247580743
Kurtosis2.268802245
Mean22.95589109
Median Absolute Deviation (MAD)1.68
Skewness0.9536114438
Sum4637.09
Variance8.202110894
MonotonicityNot monotonic
2022-12-10T10:35:12.304210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.863
 
1.5%
23.583
 
1.5%
20.152
 
1.0%
22.962
 
1.0%
22.632
 
1.0%
23.992
 
1.0%
22.352
 
1.0%
21.382
 
1.0%
23.972
 
1.0%
24.642
 
1.0%
Other values (170)180
89.1%
ValueCountFrequency (%)
16.751
0.5%
17.051
0.5%
17.061
0.5%
17.541
0.5%
17.791
0.5%
18.261
0.5%
18.291
0.5%
18.371
0.5%
18.931
0.5%
18.961
0.5%
ValueCountFrequency (%)
34.421
0.5%
33.731
0.5%
32.521
0.5%
31.931
0.5%
30.182
1.0%
29.971
0.5%
28.571
0.5%
28.131
0.5%
27.791
0.5%
27.561
0.5%

Cost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct176
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.02178218
Minimum28
Maximum200.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:12.428940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile31.84
Q143.85
median58.6
Q390.35
95-th percentile126.375
Maximum200.8
Range172.8
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation32.56533304
Coefficient of variation (CV)0.4718124049
Kurtosis1.42983951
Mean69.02178218
Median Absolute Deviation (MAD)17.55
Skewness1.183474821
Sum13942.4
Variance1060.500916
MonotonicityNot monotonic
2022-12-10T10:35:12.553913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.83
 
1.5%
80.33
 
1.5%
44.52
 
1.0%
32.62
 
1.0%
61.82
 
1.0%
126.42
 
1.0%
312
 
1.0%
156.62
 
1.0%
43.52
 
1.0%
113.52
 
1.0%
Other values (166)180
89.1%
ValueCountFrequency (%)
281
0.5%
29.71
0.5%
30.31
0.5%
30.51
0.5%
30.92
1.0%
312
1.0%
31.51
0.5%
31.71
0.5%
31.81
0.5%
32.62
1.0%
ValueCountFrequency (%)
200.81
0.5%
181.71
0.5%
171.11
0.5%
156.62
1.0%
148.91
0.5%
143.51
0.5%
136.31
0.5%
131.91
0.5%
126.42
1.0%
125.91
0.5%

PreviousClubCost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.87371287
Minimum34.36
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:12.681415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34.36
5-th percentile46.3205
Q154.6675
median63.035
Q374.75
95-th percentile86.95
Maximum106
Range71.64
Interquartile range (IQR)20.0825

Descriptive statistics

Standard deviation13.07019722
Coefficient of variation (CV)0.2014713917
Kurtosis-0.2158695977
Mean64.87371287
Median Absolute Deviation (MAD)9.295
Skewness0.3611965165
Sum13104.49
Variance170.8300553
MonotonicityNot monotonic
2022-12-10T10:35:12.800126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
789
 
4.5%
827
 
3.5%
686
 
3.0%
745
 
2.5%
675
 
2.5%
775
 
2.5%
694
 
2.0%
794
 
2.0%
664
 
2.0%
724
 
2.0%
Other values (125)149
73.8%
ValueCountFrequency (%)
34.361
0.5%
38.31
0.5%
39.031
0.5%
41.541
0.5%
41.931
0.5%
42.151
0.5%
42.951
0.5%
42.961
0.5%
45.231
0.5%
46.121
0.5%
ValueCountFrequency (%)
1061
0.5%
1021
0.5%
971
0.5%
911
0.5%
902
1.0%
891
0.5%
882
1.0%
872
1.0%
862
1.0%
852
1.0%

Height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct147
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.1039604
Minimum148.9
Maximum209.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:12.922438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum148.9
5-th percentile163.955
Q1174
median179.7
Q3186.175
95-th percentile195.17
Maximum209.4
Range60.5
Interquartile range (IQR)12.175

Descriptive statistics

Standard deviation9.734494453
Coefficient of variation (CV)0.05404930814
Kurtosis0.5717271417
Mean180.1039604
Median Absolute Deviation (MAD)6.05
Skewness-0.2007969509
Sum36381
Variance94.76038225
MonotonicityNot monotonic
2022-12-10T10:35:13.037754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1765
 
2.5%
1745
 
2.5%
179.64
 
2.0%
180.24
 
2.0%
1754
 
2.0%
1833
 
1.5%
185.63
 
1.5%
193.43
 
1.5%
1893
 
1.5%
1783
 
1.5%
Other values (137)165
81.7%
ValueCountFrequency (%)
148.91
0.5%
1491
0.5%
1561
0.5%
156.91
0.5%
157.91
0.5%
158.91
0.5%
1622
1.0%
162.51
0.5%
1631
0.5%
163.91
0.5%
ValueCountFrequency (%)
209.41
0.5%
203.41
0.5%
200.41
0.5%
198.71
0.5%
1981
0.5%
197.51
0.5%
196.61
0.5%
195.91
0.5%
195.41
0.5%
195.31
0.5%

Weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.00816832
Minimum37.8
Maximum123.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:13.169536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37.8
5-th percentile52.515
Q166.525
median74.4
Q384.125
95-th percentile96.995
Maximum123.2
Range85.4
Interquartile range (IQR)17.6

Descriptive statistics

Standard deviation13.92557403
Coefficient of variation (CV)0.1856541006
Kurtosis0.4254396687
Mean75.00816832
Median Absolute Deviation (MAD)9.15
Skewness0.2424318361
Sum15151.65
Variance193.9216121
MonotonicityNot monotonic
2022-12-10T10:35:13.287675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.44
 
2.0%
78.93
 
1.5%
72.93
 
1.5%
85.42
 
1.0%
90.32
 
1.0%
94.82
 
1.0%
62.92
 
1.0%
93.52
 
1.0%
74.32
 
1.0%
80.52
 
1.0%
Other values (154)178
88.1%
ValueCountFrequency (%)
37.81
0.5%
43.81
0.5%
45.11
0.5%
45.81
0.5%
47.41
0.5%
47.81
0.5%
49.21
0.5%
49.81
0.5%
50.91
0.5%
51.91
0.5%
ValueCountFrequency (%)
123.21
0.5%
113.71
0.5%
111.31
0.5%
108.21
0.5%
102.71
0.5%
1011
0.5%
100.21
0.5%
981
0.5%
97.92
1.0%
971
0.5%

Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct180
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.50742574
Minimum5.63
Maximum35.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-12-10T10:35:13.401907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.63
5-th percentile6.162
Q18.545
median11.65
Q318.08
95-th percentile24.8705
Maximum35.52
Range29.89
Interquartile range (IQR)9.535

Descriptive statistics

Standard deviation6.189825973
Coefficient of variation (CV)0.458253563
Kurtosis-0.1473654141
Mean13.50742574
Median Absolute Deviation (MAD)4.395
Skewness0.7652422284
Sum2728.5
Variance38.31394558
MonotonicityNot monotonic
2022-12-10T10:35:13.519614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.513
 
1.5%
9.563
 
1.5%
23.012
 
1.0%
62
 
1.0%
21.32
 
1.0%
17.712
 
1.0%
9.42
 
1.0%
11.072
 
1.0%
10.052
 
1.0%
6.562
 
1.0%
Other values (170)180
89.1%
ValueCountFrequency (%)
5.631
0.5%
5.81
0.5%
5.91
0.5%
5.931
0.5%
62
1.0%
6.031
0.5%
6.062
1.0%
6.11
0.5%
6.161
0.5%
6.21
0.5%
ValueCountFrequency (%)
35.521
0.5%
30.11
0.5%
28.831
0.5%
26.781
0.5%
26.651
0.5%
26.571
0.5%
26.51
0.5%
25.261
0.5%
25.161
0.5%
24.971
0.5%

Interactions

2022-12-10T10:35:08.794696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:54.573920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:56.977836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.419490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.869494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.407558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.899713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.486354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.575259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.685481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.765456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.889802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:54.720243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.104606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.552561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.014113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.539455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.075500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.587023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.700035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.784063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.859372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.979640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:54.846279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.225662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.676018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.147099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.663389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.254244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.688159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.793851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.875803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.946609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.071847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:54.982030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.354163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.811931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.278946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.795930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.392155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.782994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.894469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.980104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.038827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.169054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:55.141757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.506564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.953079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.420593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.931512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.782364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.886948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.997725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.078836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.132236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.265102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:55.307914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.634108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.080539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.552384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.062459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.878032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.976478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.100914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.171765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.220057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.365449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:55.458707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.768575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.210855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.698475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.199177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:03.986320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.082201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.204032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.272021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.324273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.467191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:55.592521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:57.903233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.345429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.847545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.330790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.094084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.181702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.303519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.369088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.416694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:09.565491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:55.736369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.029630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.475400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:00.989823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.460077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.196068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.283440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.403778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.476334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.506388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:10.005785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:56.705985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.164355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.605278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.133562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.597653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.298777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.381888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.500711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.576462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.602783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:10.109381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:56.836896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:58.286520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:34:59.724163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:01.260961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:02.724437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:04.386021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:05.476840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:06.589377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:07.667751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-10T10:35:08.690618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-12-10T10:35:13.633332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-10T10:35:13.815902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-10T10:35:13.974289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-10T10:35:14.139744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-10T10:35:10.304074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-10T10:35:10.505548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PlayerNameClubDistanceCovered(InKms)GoalsMinutestoGoalRatioShotsPerGameAgentChargesBMICostPreviousClubCostHeightWeightScore
0Braund, Mr. Owen HarrisMUN3.967.537.512.36020.56109.163.32195.978.919.75
1Allen, Mr. William HenryMUN4.418.338.212.76820.67102.858.55189.774.421.30
2Moran, Mr. JamesMUN4.145.036.411.62121.86104.655.36177.869.119.88
3McCarthy, Mr. Timothy JMUN4.115.337.312.66921.88126.457.18185.074.923.66
4Palsson, Master. Gosta LeonardMUN4.456.841.514.02918.9680.353.20184.664.617.64
5Saundercock, Mr. William HenryMUN4.104.437.412.54221.0475.253.77174.063.715.58
6Andersson, Mr. Anders JohanMUN4.315.339.612.87321.6987.260.17186.275.219.99
7Rice, Master. EugeneMUN4.425.739.913.24420.6297.948.33173.862.322.43
8Williams, Mr. Charles EugeneMUN4.308.941.113.54122.6475.154.57171.466.517.95
9Fynney, Mr. Joseph JMUN4.514.441.612.74419.4465.153.42179.962.915.07

Last rows

PlayerNameClubDistanceCovered(InKms)GoalsMinutestoGoalRatioShotsPerGameAgentChargesBMICostPreviousClubCostHeightWeightScore
192Rommetvedt, Mr. Knud PaustLIV5.046.045.915.05221.2643.369.0187.774.97.82
193Rosblom, Mr. Viktor RichardLIV4.6314.344.815.013325.4349.579.0185.387.38.97
194Rouse, Mr. Richard HenryLIV5.117.047.715.821424.5470.080.0191.590.011.63
195Rush, Mr. Alfred George JohnLIV5.346.249.817.214327.7975.782.0184.694.713.49
196Ryan, Mr. EdwardLIV4.868.946.915.86523.5857.768.0179.976.310.25
197Ryan, Mr. PatrickLIV4.907.645.616.09027.5667.282.0183.993.211.79
198Saad, Mr. AminLIV5.668.350.217.73823.7656.572.0183.580.010.05
199Saad, Mr. KhalilLIV5.036.442.714.312222.0147.668.0183.173.88.51
200Saade, Mr. Jean NassrLIV4.978.843.014.923322.3460.463.0178.471.111.50
201Sadlier, Mr. MatthewLIV5.386.346.015.73221.0734.972.0190.876.76.26